Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 709-715, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-709-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 709-715, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-709-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jun 2016

10 Jun 2016

A CONVOLUTIONAL NETWORK FOR SEMANTIC FACADE SEGMENTATION AND INTERPRETATION

Matthias Schmitz and Helmut Mayer Matthias Schmitz and Helmut Mayer
  • Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany

Keywords: Convolutional Network, Deep Learning, Facade Interpretation, Object Detection, Segmentation

Abstract. In this paper we present an approach for semantic interpretation of facade images based on a Convolutional Network. Our network processes the input images in a fully convolutional way and generates pixel-wise predictions. We show that there is no need for large datasets to train the network when transfer learning is employed, i. e., a part of an already existing network is used and fine-tuned, and when the available data is augmented by using deformed patches of the images for training. The network is trained end-to-end with patches of the images and each patch is augmented independently. To undo the downsampling for the classification, we add deconvolutional layers to the network. Outputs of different layers of the network are combined to achieve more precise pixel-wise predictions. We demonstrate the potential of our network based on results for the eTRIMS (Korč and Förstner, 2009) dataset reduced to facades.